Wepresent a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops
policies from example state to action mappings. We introduce the LfD design choices in terms of
demonstrator, problem space, policy derivation and performance, and contribute the foundations for a
structure in which to categorize LfD research. Specifically, we analyze and categorize the multiple ways
in which examples are gathered, ranging from teleoperation to imitation, as well as the various techniques
for policy derivation, including matching functions, dynamics models and plans. To conclude we discuss
LfD limitations and related promising areas for future research.